import cv2
import numpy as np
import HandTrackingModule as htm
import time
import autopy
##########################
wCam, hCam = 640, 480
frameR = 100 # Frame Reduction
smoothening = 7
#########################

pTime = 0
plocX, plocY = 0, 0
clocX, clocY = 0, 0

cap = cv2.VideoCapture(1)
cap.set(3, wCam)
cap.set(4, hCam)
detector = htm.handDetector(maxHands=1)
wScr, hScr = autopy.screen.size()
# print(wScr, hScr)

while True:
# 1. Find hand Landmarks
success, img = cap.read()
img = detector.findHands(img)
lmList, bbox = detector.findPosition(img)
# 2. Get the tip of the index and middle fingers
if len(lmList) != 0:
x1, y1 = lmList[8][1:]
x2, y2 = lmList[12][1:]
# print(x1, y1, x2, y2)

# 3. Check which fingers are up
fingers = detector.fingersUp()
# print(fingers)
cv2.rectangle(img, (frameR, frameR), (wCam – frameR, hCam – frameR),
(255, 0, 255), 2)
# 4. Only Index Finger : Moving Mode
if fingers[1] == 1 and fingers[2] == 0:
# 5. Convert Coordinates
x3 = np.interp(x1, (frameR, wCam – frameR), (0, wScr))
y3 = np.interp(y1, (frameR, hCam – frameR), (0, hScr))
# 6. Smoothen Values
clocX = plocX + (x3 – plocX) / smoothening
clocY = plocY + (y3 – plocY) / smoothening

# 7. Move Mouse
autopy.mouse.move(wScr – clocX, clocY)
cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
plocX, plocY = clocX, clocY

# 8. Both Index and middle fingers are up : Clicking Mode
if fingers[1] == 1 and fingers[2] == 1:
# 9. Find distance between fingers
length, img, lineInfo = detector.findDistance(8, 12, img)
print(length)
# 10. Click mouse if distance short
if length < 40:
cv2.circle(img, (lineInfo[4], lineInfo[5]),
15, (0, 255, 0), cv2.FILLED)
autopy.mouse.click()

# 11. Frame Rate
cTime = time.time()
fps = 1 / (cTime – pTime)
pTime = cTime
cv2.putText(img, str(int(fps)), (20, 50), cv2.FONT_HERSHEY_PLAIN, 3,
(255, 0, 0), 3)
# 12. Display
cv2.imshow(“Image”, img)
cv2.waitKey(1)
